- Code ENGN8536
- Unit Value 6 units
- Offered by Research School of Engineering
- ANU College ANU College of Engineering and Computer Science
- Course subject Engineering
- Academic career PGRD
- AsPr Nicholas Barnes
- Dr Jonghyuk Kim
- Mode of delivery In Person
Second Semester 2018
See Future Offerings
The course will describe the theory and practice of deep Neural Networks, otherwise known as Deep Learning, with a particular emphasis on their use in Image Processing and Computer Vision.
Upon successful completion, students will have the knowledge and skills to:On successful completion of this course, students should be able to:
- Describe and apply the theory of convolutional neural networks and the universal approximation theorem
- Apply the theory of training a convolutional neural network.
- Design and train neural networks to mimic simple logical operations and solve simple applications of such logical problems.
- Competently use major software tools in the area of deep neural networks including matconvnet and a matlab based tool for deep learning.
- Apply programming and software tools to solve realistic problems of image processing using deep neural networks.
- Write programs to design and test neural networks when applied to complex logical tasks.
- Research an area of image processing and apply deep neural network methods to a problem in that area.
Professional Skills Mapping
Mapping of Learning Outcomes to Assessment and Professional Competencies
Indicative AssessmentProblem sets 20%; Project report 40%; Final exam 40%
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Workload12 × 2 hr Lectures, 6 × 2 hr Tutorials
Requisite and Incompatibility
Assumed KnowledgeMathematics including differential equations, probability theory, statistics, and matrix analysis. Students are also required to have adequate programming and software skills.
Tuition fees are for the academic year indicated at the top of the page.
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- Student Contribution Band:
- Unit value:
- 6 units
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Offerings, Dates and Class Summary Links
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Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|8579||23 Jul 2018||30 Jul 2018||31 Aug 2018||26 Oct 2018||In Person||N/A|